Scalp High Frequency Oscillations (HFOs) are promising biomarkers of epileptogenic zones. Since HFOs visual detection is strenuous, there is a real need to develop accurate HFOs automatic detectors. In this paper, we present a comparative study of two detectors: onedimensional (1D) Convolutional Neural Networks (CNN) running on High-Density Electroencephalograms signals and two dimensional (2D) CNN on time-frequency maps of those signals. Experimental results show that 1DCNN enables easy end-to-end learning of preprocessing, feature extraction and classification modules while achieving competitive performance.